Action-Bound AI Safety: A Pre-Commitment Runtime Framework for Physical, Cyber-Physical, and Transactional Systems

This manuscript proposes Action-Bound AI Safety, a pre-commitment runtime framework for physical, cyber-physical, transactional, and agentic systems. The central claim is narrow: runtime oversight can improve intervention success when monitoring occurs before externally consequential commitment, provided three conditions remain available — usable signal, enough time, and retained intervention authority. The framework introduces commitment boundaries, pre-action buffers, phase-sensitive escalation, Safety Slack (S_t; Naing's Equation), and commitment gates. It treats runtime safety as a control problem: whether the system can detect risk early enough, interpret it reliably enough, and still possess authority to halt, gate, roll back, throttle, or safely degrade an action before it becomes irreversible. The manuscript is presented as a theory-first engineering framework and falsifiable research program, not as empirical validation, deployable safety software, or a completed mathematical proof of safety.